tux snipes-hesitation

  • If the code is good enough you wouldn’t know, but then it’s likely the LLM code was audited and fixed by a skilled human so 🤷‍♂️.

    In my experience there are smells:

    • Comments are oddly explanatory of basic concepts.
    • Making an unusually large number of changes in a single commit (specifically, changes that arent related to the goal of the commit)
    • Writing new infrastructure code that duplicates the work of old code for no reason.
    • Using old library or language conventions unexpectedly.

    Most importantly:

    • A contributor who has no idea what their code does.

    A lot of it you learn from using AI yourself. I use it for inline hints, and i see what goes wrong in that and from testing agentic crap - the stuff that stinks of bad AI use that you can then see in the work of colleagues.